Application of Deep Learning Approaches for Sentiment Analysis

Pathak, Ajeet Ram; Agarwal, Basant; Pandey, Manjusha; Rautaray, Siddharth Swarup

doi:10.1007/978-981-15-1216-2_1

Cited by 17 publications

(11 citation statements)

References 34 publications

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“…In stage 2, founded in 2011, Brand24.com is an analytical monitoring technique that uses social media and the web to screen search keywords and refine them for better output such as mentions, sources and then filters the results under negative and positive sentiments. Also, sentiment analysis scrutinizes people's opinions toward entities such as products, services, persons, and organizations present in the text (Pathak et al, 2020). So, to examine the positive spillovers of crises on persons, organizations, and destinations, the technique analyzed three search terms (keywords) for political and technological crises, found online for twelve months.…”

Section: Discussionmentioning

confidence: 99%

“…Online-contents are very informative in producing sound results with easy access and do not allow researchers' bias that hinders respondents' opinions or wiliness of expression as with questionnaires (Gemzik-Salwach, 2020). Recall that previous studies have achieved satisfactory results using the Brand24.com tool, for instance, (Augustyniak, Rajda, Kajdanowicz, & Bernaczyk, 2020;Bachmann, 2020;Pathak, Agarwal, Pandey, & Rautaray, 2020;Lee, Buchanan, & Yu, 2020).…”

Section: Data Collectionmentioning

confidence: 92%

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Positive Spillover of Political and Technological Crises on Persons, Organizations, and Destinations across Covid-19: A Data Triangulation Study

Onyeama¹

2021

OJPS

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Crises are now parts of daily experience, peoples, organizations, and destinations experience crises. Most times, it occurs without realizing it, and even when people take note, it is the negative spillovers that are often emphasized more than the positive spillovers. Little do people realize that the crisis they are in today prepares them for tomorrow's strength. Unfortunately, there is little or no literature on the positive spillovers of crises. This study aims to fill the literature on the positive spillover of crises on peoples, organizations, and destinations using a qualitative data triangulation, comprising face-to-face interviews and textual data. The study employed spillover effect and resilience theories to justify the arguments. Findings from stage 1 analysis revealed three themes, and stage 2 analyzes supported the findings. Most times, survivors of crises end up becoming resilient after an experience. This study discussed managerial and practical implications, as well as limitations and future studies.

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Section: Discussionmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 92%

Positive Spillover of Political and Technological Crises on Persons, Organizations, and Destinations across Covid-19: A Data Triangulation Study

Onyeama¹

2021

OJPS

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“…The sentiment analysis of text can be divided into three categories, namely, the phrase level, sentence level, and document level [6]. According to the sentiment tendency of features, sentiment classification can easily be performed on the phrase and sentence levels [7].…”

Section: Introductionmentioning

confidence: 99%

Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

et al. 2022

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Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.

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“…One of the biggest challenges concerning the sentiment classification of tweets is that people often express their sentiments and opinions using a casual linguistic style, resulting in the presence of misspelling words and the careless use of grammar. Consequently, the automated analysis of tweets' content requires machines to build a deep understanding of natural text to deal effectively with its informal structure (Pathak et al 2020). However, before discovering patterns from text, it is essential to define a more fundamental step: how automatic methods can numerically represent textual content.…”

Section: Introductionmentioning

confidence: 99%

Sentiment analysis in tweets: an assessment study from classical to modern word representation models

Barreto

Moura

Carvalho

et al. 2022

Data Min Knowl Disc

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With the exponential growth of social media networks, such as Twitter, plenty of user-generated data emerge daily. The short texts published on Twitter -the tweets -have earned significant attention as a rich source of information to guide many decision-making processes. However, their inherent characteristics, such as the informal, and noisy linguistic style, remain challenging to many natural language processing (NLP) tasks, including sentiment analysis. Sentiment classification is tackled mainly by machine learning-based classifiers. The literature has adopted different types of word representation models to transform tweets to vector-based inputs to feed sentiment classifiers. The representations come from simple count-based methods, such as bag-of-words, to more sophisticated ones, such as BERTweet, built upon the trendy BERT architecture. Nevertheless, most studies mainly focus on evaluating those models using only a small number of datasets. Despite the progress made in recent years in language modeling, there is still a gap regarding a robust evaluation of induced embeddings applied to sentiment analysis on tweets. Furthermore, while fine-tuning the model from downstream tasks is prominent nowadays, less attention has been given

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Application of Deep Learning Approaches for Sentiment Analysis

Cited by 17 publications

References 34 publications

Positive Spillover of Political and Technological Crises on Persons, Organizations, and Destinations across Covid-19: A Data Triangulation Study

Positive Spillover of Political and Technological Crises on Persons, Organizations, and Destinations across Covid-19: A Data Triangulation Study

Document-Level Sentiment Analysis Using Attention-Based Bi-Directional Long Short-Term Memory Network and Two-Dimensional Convolutional Neural Network

Sentiment analysis in tweets: an assessment study from classical to modern word representation models

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